(616b) Strategic Long-Term Energy Planning for Natural Gas to Liquid Fuels (GTL) Supply Chain Systems

Interests on natural gas in the United States energy sector have increased in recent years due to the discoveries of shale gas coupled with advancements in horizontal drilling and fracking technologies. The increase in production has lower the cost of natural gas, making it an attractive feedstock for fuels and energy production in the near term. With the abundant natural gas reserves in the country, natural gas is slated to continue as a major energy source for the following decades.

Recently, optimization frameworks for energy supply chain analyses based on hybrid coal, biomass, and natural gas to liquids (CBGTL) and natural gas to liquids (GTL) processes have been proposed [1-3]. The framework includes the locations of the feedstocks in the United States discretized by county, the delivery locations of fuel products, the transportation costs of every input and output of the refinery, the material balances of each refinery, water resources, electricity requirement or production for the supply chain, and the CO₂ sequestration capacities in the United States. Formulated as a large-scale mixed-integer linear optimization (MILP) model, the solution gives the strategic locations for the refineries that minimize the total cost of fuel production. The frameworks, however, do not consider the temporal aspect of the problem, namely the different phases of the decisions to implement and build the refineries over a long time horizon.

An optimization-based deterministic strategic planning approach for GTL systems in the United States is proposed in which decisions are made over a 30-year horizon. These decisions include the number of the new refineries built, their locations, capacities, and sources of feedstocks and product flowrates.  The 30-year horizon is divided into 10 periods and the decisions are made each period. Multiple capacities of the GTL refineries are considered (i.e., 1, 5, 10, 50, and 200 thousand barrels per day) as well as different fuel ratios produced (i.e., commensurate with the United States demand, maximized diesel production, maximized kerosene production, or unrestricted fuel composition) [4-5]. The model is formulated as a large-scale MILP model that maximizes the net present value of the overall network. Case studies based on states and state groupings are presented.

1. J. A. Elia, R. C. Baliban, X. Xiao, C. A. Floudas. Optimal energy supply network determination and life cycle analysis for hybrid coal, biomass, and natural gas to liquid (CBGTL) plants using carbon-based hydrogen production. Comp. Chem. Eng., 2011:35,1399-1430.

2. J.A. Elia, R.C. Baliban, C.A. Floudas.  Nationwide Energy Supply Chain Analysis for Hybrid Feedstock Processes with Significant CO₂ Emissions Reduction. AIChE J., 2012:58, 2142-2154.

3. J. A. Elia, R. C. Baliban, C. A. Floudas. Nationwide, Regional, and Statewide Energy Supply Chain Optimization for Natural Gas to Liquid Transportation Fuel (GTL) Systems.  Ind. Eng. Chem. Res., 2013, under review.

4. R.C. Baliban, J.A. Elia, C.A. Floudas.  Novel Natural Gas to Liquids Processes: Process Synthesis and Global Optimization Strategies.  AIChE J., 2013:59, 505-531.

5. R.C. Baliban, J.A. Elia, V.W. Weekman, C.A. Floudas.  Process synthesis of hybrid coal, biomass, and natural gas to liquids via Fischer-Tropsch synthesis, ZSM-5 catalytic conversion, methanol synthesis, methanol-to-gasoline, and methanol-to-olefins/distillate technologies.  Comp. Chem. Eng., 2012:47, 29-56.

6. Verderame, P. M., Elia, J. A., Li, J., Floudas, C. A. Planning and scheduling under uncertainty: A review across multiple sectors. Industrial & Engineering Chemistry Research 2010:49, 3993-4017.